Image reconstruction in region-of-interest (or interior) digital tomosynthesis (DTS) based on compressed-sensing (CS)

• Published in: Computer methods and programs in biomedicine Vol. 151; pp. 151 - 158
• Main Authors: Park, Soyoung, Kim, Guna, Cho, Hyosung, Je, Uikyu, Park, Chulkyu, Kim, Kyuseok, Lim, Hyunwoo, Lee, Dongyeon, Lee, Hunwoo, Kang, Seokyoon, Park, Jeongeun, Woo, Taeho, Lee, Minsik
• Format: Journal Article
• Language: English
• Published: Ireland Elsevier B.V 01.11.2017
• Subjects: Algorithms; Compressed-sensing; Digital tomosynthesis; Dose reduction; Humans; Image Processing, Computer-Assisted; Phantoms, Imaging; Region-of-interest; Tomography, X-Ray Computed; Digital tomosynthesis; Region-of-interest; Compressed-sensing; Dose reduction
• ISSN: 0169-2607; 1872-7565; 1872-7565
• DOI: 10.1016/j.cmpb.2017.08.022

•A new type of digital tomosynthesis (DTS) examination was introduced.•DTS region-of-interest (ROI) reconstruction requires less radiation dose.•A CS-based algorithm was used for ROI-DTS image reconstruction.•The new algorithm was tested on a laboratory prototype DTS system. Digital tomosynthesis (DTS) based on filtered-backprojection (FBP) reconstruction requires a full field-of-view (FOV) scan and relatively dense projections, which results in high doses for medical imaging purposes. To overcome these difficulties, we investigated region-of-interest (ROI) or interior DTS reconstruction where the x-ray beam span covers only a small ROI containing a target area. An iterative method based on compressed-sensing (CS) scheme was compared with the FBP-based algorithm for ROI-DTS reconstruction. We implemented both algorithms and performed a systematic simulation and experiments on body and skull phantoms. The image characteristics were evaluated and compared. The CS-based algorithm yielded much better reconstruction quality in ROI-DTS compared to the FBP-based algorithm, preserving superior image homogeneity, edge sharpening, and in-plane resolution. The image characteristics of the CS-reconstructed images in ROI-DTS were not significantly different from those in full-FOV DTS. The measured CNR value of the CS-reconstructed ROI-DTS image was about 12.3, about 1.9 times larger than that of the FBP-reconstructed ROI-DTS image. ROI-DTS images of substantially high accuracy were obtained using the CS-based algorithm and at reduced imaging doses and less computational cost, compared to typical full-FOV DTS images. We expect that the proposed method will be useful for the development of new DTS systems.